For centuries, people have been harnessing the power of analytics to make better decisions. In fact, the process dates back to Greek philosopher Aristotle’s work on logic.
In layman’s terms, analytics is the systematic examination of data and statistics used to make educated business decisions. But while people in Aristotle’s time had only paper and pen to work with, modern organizations can leverage the Industrial Internet of Things (IIoT), Industry 4.0, artificial intelligence, and machine learning for better analytic applications and even more valuable data.
Despite these advancements, organizations across all industries must continuously be on the lookout for opportunities for improvement, given the lack of context in most data combined with the vast amount and types of data points. A data analytics strategy centered around continuous improvement can boost efficiency, maximize output, and improve the bottom line. Thus, organizations that develop a mature data analytics strategy can experience continuous quality and process surge in performance.
As we have witnessed over the last few years, companies that focus on utilizing the latest technology to meet supply chain demands outperform companies that don’t.
In fact, Renwood Mills yielded immediate benefits by decreasing down-time in their production lines. In just one year, they saved almost $100,000 on just one line with Sightline EDM data analytics and root cause analysis solution.
Your company’s goal should be to never stop improving. A data analytics strategy can make that possible.
The Importance of Data Analytics Strategies for Continuous Improvement
There is vast potential for continuous improvement in today’s standard operations. With its roots in the manufacturing industry, continuous improvement enables businesses to continuously identify opportunities for ensuring efficiency. This not only benefits employees and customers, but also investors and stakeholders.
When considering the benefits of data analytics in your operational processes, consider all of the moving parts throughout production. From a manufacturing, industrial, and aquaculture lens, there are hundreds—if not thousands—of processes that can be made more efficient. From relieving the pressure throughout the plant from the current labor shortage and creating more accurate forecasts to preventing disease spread throughout fisheries and monitoring water quality to prevent kill off, data analytics is proven to decrease expenses by 47% and improve operational processes by 54%.
While all organizations strive for continuous quality improvement, many are not successful at achieving it through manual processes. Continuous improvement is not a one-and-done deal. The process does not have a clear-cut end.
It’s also one filled with roadblocks, including communication hurdles that hinder collaboration, limited budgets, and a lack of problem-solving capabilities. Digital advancements, including big data management tools and data analytics software, can help organizations foster a culture of continuous improvement in less time.
The secret to success could be buried in the avalanche of data that your company has already gathered. However, it’s essential that organizations understand how to leverage these hefty amounts of data in the proper way. Developing the right data analytics strategy will help set the wheels in motion.
How to Gain a Competitive Edge by Leveraging Data Analytics
Many organizations use data analytics to glean deeper insights into distribution, production, or customer activities. However, analytics can be used for much more than that. It can:
- Be used to make confident business decisions in real-time
- Enable financial and operational planning and forecasting via predictive analytics
- Create hierarchically structured data used for regulatory compliance
In order to make your big data work harder, your organization must have a comprehensive data analytics strategy in place to establish a single source of truth for improved collaboration and continuous process improvement.
Developing a single source of truth via a data analytics strategy allows organizations to better understand and communicate the context of every data element, including its source, lineage, definition, and type.
For instance, the sales and finance departments of a manufacturing organization could both report the profit metric to the executive board. Unfortunately, their numbers can be different. That’s because the sales department may be reporting gross profit and finance is using net profit. This is a problem in both the data lineage and definition. This may seem like such a simple solution, but we have seen time and time again where various departments across the same organization use the same terms to refer to different data points.
A data analytics strategy can remove these communication roadblocks and allow a company to efficiently ask and answer essential data-informed business questions.
How to Develop an Effective Data Analytics Strategy
Organizations can develop an effective data analytics strategy for continuous improvement in five actionable steps, including:
1. Identifying key players: Implementing a successful data analytics strategy requires a cross-functional team of committed and cognisant stakeholders who can lead by example. They should have a vested interest in the organization’s data and long-term goals of how to best use the information.
2. Initial discovery sessions: Like any new strategy, an organization needs to conduct several initial discovery sessions to learn more about its current data analytics processes. These sessions should examine the current state of the business’s big data sets, technologies, and current use cases. This helps an organization better understand what data is worth tracking for continuous improvements.
3. Establishing an analytics operating model and picking the right tools: While there are many analytics operating models to consider, such as functional and CoE, think about what tools will be most effective for analytics consumption. Stakeholders should consider the cost, security, scalability, and user-friendliness of each tool. According to Deloitte’s Global CIO survey, 69% of organizations are improving their business operations with cloud-based platforms that have built-in data security.
4. Enhancing company wide data literacy: Every associate at an organization needs to understand data best practices. While this is the most challenging element of establishing a data analytics strategy, it is also the most crucial. The stakeholders must lead by example, the company should train from within, and data should be accessible to all. Having a single, easy-to-understand dashboard with optional drill-down areas right out of the box helps employees understand important data points faster and see their correlations sooner.
5. Review and revise: No strategy will be an initial success. The organization must continuously review its data analytics strategy to identify opportunities for improvement.
Develop a Single Source of Data Truth with Sightline EDM
Finding an advanced data analytics software that can be implemented across all of your businesses facilities is vital to keep up with supply chain demands and increase productivity. With Sightline EDM, your organization can develop a deeper understanding of its most pertinent data and uncover likely future events through advanced analytics. Gain a 360-degree view of your operation’s entire data ecosystem on one accessible, secure, and cost-effective dashboard that your entire team can easily use.
To learn more or to schedule a free demo, contact us today.